Machine Learning Interatomic Potentials (MLIPs) enable first-principles accuracy in modeling complex catalytic interfaces at reduced cost. The Open Catalyst 2020 (OC20) and Open Materials 2024 (OMat24) datasets consist of density functional theory (DFT) data for adsorbates on metals and inorganic bulk materials, respectively. In this work, we benchmarked the Universal Model for Atoms (UMA) trained on the OC20 and OMat24 datasets with DFT calculations for adsorption energies on Pt and IrO_2 , respectively. We evaluated adsorption energies of common adsorbates such as *H, *O, *CO, and *NO on Pt, and reaction intermediates in the water splitting reaction, such as *O, *OH, and *H, on IrO_2 . UMA-OC20, trained using non-spin polarized DFT-RPBE energies with no van der Waals (vdW) corrections on metallic surfaces, reproduced the adsorption energies from the same flavor of DFT within 0.1 eV. However, the energy difference between UMA-OC20 and spin-polarized DFT was ∼ 1.2 eV, with the difference mainly arising from the reference gases. UMA-OMat24, trained using spin-polarized DFT-PBE with vdW corrections, predicted adsorption energies within 0.3 eV of the energies from the same flavor of DFT on IrO_2 . When both the DFT flavor and geometry were kept fixed between the MLIPs and DFT, UMA-OC20 and UMA-OMat24 predicted adsorption energies within 0.1 and 0.2 eV of their corresponding DFT adsorption energies, respectively. The norm of coordinate differences between UMA-OC20 and DFT was ∼ 0.1 Å for a system size of ∼ 20 atoms, while it was < 0.6 Å for a system size of ∼ 60 atoms, between UMA-OMat24 and DFT. MLIP requires considerably fewer ( ∼ 10^3-10^4 times less) CPU-hours compared to DFT for geometry optimization. Thus, we recommend opting for an MLIP-followed-by-DFT procedure, as it not only reduces the total compute time but also ensures accurate geometry and energies.

Machine Learning Interatomic Potentials (MLIPs) enable first-principles accuracy in modeling complex catalytic interfaces at reduced cost. The Open Catalyst 2020 (OC20) and Open Materials 2024 (OMat24) datasets consist of density functional theory (DFT) data for adsorbates on metals and inorganic bulk materials, respectively. In this work, we benchmarked the Universal Model for Atoms (UMA) trained on the OC20 and OMat24 datasets with DFT calculations for adsorption energies on Pt and IrO 2 , respectively. We evaluated adsorption energies of common adsorbates such as *H, *O, *CO, and *NO on Pt, and reaction intermediates in the water splitting reaction, such as *O, *OH, and *H, on IrO 2 . UMA-OC20, trained using non-spin polarized DFT-RPBE energies with no van der Waals (vdW) corrections on metallic surfaces, reproduced the adsorption energies from the same flavor of DFT within 0.1 eV. However, the energy difference between UMA-OC20 and spin-polarized DFT was ∼1.2 eV, with the difference mainly arising from the reference gases. UMA-OMat24, trained using spin-polarized DFT-PBE with vdW corrections, predicted adsorption energies within 0.3 eV of the energies from the same flavor of DFT on IrO 2 . When both the DFT flavor and geometry were kept fixed between the MLIPs and DFT, UMA-OC20 and UMA-OMat24 predicted adsorption energies within 0.1 and 0.2 eV of their corresponding DFT adsorption energies, respectively. The norm of coordinate differences between UMA-OC20 and DFT was < 0.005 Å while it was < 0.3 Å between UMA-OMat24 and DFT. MLIP requires considerably fewer (∼6000 times less) CPU-hours compared to DFT. Thus, we recommend opting for an MLIP-followed-by-DFT procedure, as it not only reduces the total compute time but also ensures accurate geometry and energies.

Nanoconfined electrocatalysts demonstrate enhanced selectivity and activity owing to new phenomena emerging from chemical species confined at the interface. Creating these nanoconfined pockets at the interface is not trivial. We need highly negative potentials to create the pockets in metal/ligand catalysts like Ag-nanoparticle/ordered ligand interlayer catalysts. Lowering this activation potential can make the electrocatalysis more energy-efficient, and changes in the environment can impact this potential. We used Density Functional Theory (DFT) to evaluate the impact of ligand length and density, interfacial protons, and surface defects like Ag vacancies and Cu and Au dopants. The nanoconfined configuration is stabilized when the ligands are longer and more densely packed, leading to better agglomeration. It is also stabilized when the ligands adsorb protons and when the ligands detach from the surface easily owing to lower charge transfer. Au-doped surfaces displayed the lowest charge transfer and decreased the activation potential.

Real-space Kohn-Sham density functional theory (real-space KS-DFT) enables large-scale electronic structure simulations that is particularly well-suited for the modern high-performance computing (HPC) architectures. This feature article reviews its theoretical foundations, highlights the algorithmic advances and recent developments, and showcases applications in complex nano systems. We aim to provide a perspective on the trajectory of real-space KS-DFT as an emerging tool for computational chemistry and materials science in the exascale era.

Directly visualizing chemical trajectories offers novel insights into catalysts, gas phase reactions, photo-induced dynamics, and quantum information processing. Identifying and tracking the exchange of matter to observe the creation and annihilation of chemical species is best achieved by closely coupling theory and experiment. We developed Digital Twin for Chemical Science (DTCS) v.01, a platform that mimics advanced characterization instruments, including those at Scientific User Facilities. DTCS v.01 addresses challenges in data acquisition, analysis, and model-driven interpretation via a physics-based, AI-accelerated approach. We validated this concept with ambient pressure X-ray Photoelectron Spectroscopy (APXPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O, as a representative example. The inputs of DTCS v.01 are designed to mirror the experimental chemists' workflows, and the outputs can be directly compared to and are constantly updated from the experimental data. This integrated theoretical and experimental platform enhances user accessibility and facilitates the acquisition of standardized mechanistic insights.

Aqueous aerosols are useful model systems for understanding the physical chemistry that takes place in the Earth's atmosphere, as well as the chemistry of other solar system objects. Ammonium sulfate aerosols are impacted by anthropogenic sources such as farming and shipping and are understood to be important seeds of cloud nucleation in the atmosphere, affecting climate. X-ray photoelectron spectroscopy and near edge x-ray absorption fine structure spectroscopy were used in tandem to probe the surface and bulk properties of aqueous (NH4)2SO4, respectively. Aerosolized solutions of (NH4)2SO4, some altered with NaOH to modify pH, were introduced via an aerodynamic lens to a velocity map imaging instrument for detection. The results show that as pH and sodium cation concentration increase, sulfate anion and ammonia become more prevalent at the surface, and the aerosol surface appears to become drier. However, most of the aerosol remains aqueous, with ion concentrations insensitive to the addition of NaOH. Density functional theory calculations were also performed to probe the coordination environment at the aerosol surfaces to understand the underlying phenomena. The changes seen on the surface of the aerosol underscore the importance of pH in regulating the structure and chemical properties of atmospherically relevant aerosols.

Developing efficient sorbent systems with a high CO2 adsorption capacity and ease of regeneration is crucial for carbon capture. This work presents a bioinspired approach using three-dimensional (3D) porous carbon derived from abundant Balsa wood. CO2 on these materials has been systematically investigated using kinetic characterization, in situ Fourier-transformed infrared (FTIR) spectroscopy, and theoretical calculations. The 3D carbon materials possess a high surface area and abundant hydroxyl (OH) groups, which act as basic sites to interact with acidic CO2, significantly enhancing the CO2 adsorption capacity. Specifically, KOH-treated Balsa carbon could achieve a CO2 adsorption capacity of 4.1 mmol g(-1) at 600 mbar, outperforming other carbon-based adsorbents. In situ FTIR spectroscopy confirmed that CO2 adsorption is predominantly chemisorptive, forming carbonate and bicarbonate species. Efficient CO2 desorption under mild conditions (<85 degrees C) and negligible performance degradation over 11 cycles indicate good stability and reusability. Density functional theory calculations supported the experimental findings, showing favorable chemisorption with an adsorption energy of -0.64 eV for an OH-functionalized model carbon surface. This study highlights the importance of surface functionalization in enhancing the CO2 adsorption capacity and provides insights for designing advanced carbon-based sorbents. This work also demonstrates the potential of 3D porous carbon from Balsa wood as a high-performance CO2 sorbent, offering a sustainable and efficient solution for carbon capture and contributing to global efforts to reduce atmospheric CO2 and mitigate climate change.

Semiconductor photoelectrochemistry is a dynamic and interdisciplinary field at the forefront of research in solar fuels, energy conversion, and catalysis. This Perspective captures the collective insights from the second Gerischer Electrochemistry Today Symposium, held at Colorado State University in Fort Collins, CO, in August 2024, which convened leading researchers, early-career scientists, and industry partners to define the critical next steps for the field. Through interactive sessions, technical talks, panel discussions, and training initiatives-including a Semiconductor Electrochemistry Bootcamp-the symposium emphasized three pillars of advancement: (i) facilitating the exchange of new ideas in semiconductor electrochemistry and charge separation; (ii) fostering the development of future researchers, research topics, and participation in the semiconductor workforce; and (iii) building community. This Energy Focus distills key themes from the meeting and identifies major knowledge gaps in the following areas: mechanisms of charge separation and recombination, role of defects and disorder, dynamic and operando characterization methods, interfacial chemistry and surface passivation, theoretical and modeling limitations, and standardization and benchmarking. The inclusive and collaborative structure of the symposium enabled the generation of this comprehensive report that will serve as a roadmap for fundamental and applied research in the rapidly evolving field of semiconductor electrochemistry over the next decade.

Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag-H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.

The dynamic response of surface ligands on nanoparticles (NPs) to external stimuli critically determines the functionality of NP–ligand systems. For example, in electrocatalysis the collective dissociation of ligands on NP surfaces can lead to the creation of an NP/ordered-ligand interlayer, a microenvironment that is highly active and selective for CO 2 -to-CO conversion. However, the lack of in situ characterization techniques with high spatial resolution hampers a comprehensive molecular-level understanding of the mechanism of interlayer formation. Here we utilize in situ infrared nanospectroscopy and surface-enhanced Raman spectroscopy, unveiling an electrochemical bias-induced consecutive bond cleavage mechanism of surface ligands leading to formation of the NP/ordered-ligand interlayer. This real-time molecular insight could influence the design of confined localized fields in multiple catalytic systems. Moreover, the demonstrated capability of capturing nanometre-resolved, dynamic molecular-scale events holds promise for the advancement of using controlled local molecular behaviour to achieve desired functionalities across multiple research domains in nanoscience.

Directly visualizing chemical trajectories offers novel insights into catalysts, gas phase reactions, photo-induced dynamics, and quantum information processing. Identifying and tracking the exchange of matter to observe the creation and annihilation of chemical species is best achieved by closely coupling theory and experiment. We developed Digital Twin for Chemical Science (DTCS) v.01, a platform that mimics advanced characterization instruments, including those at Scientific User Facilities. DTCS v.01 addresses challenges in data acquisition, analysis, and model-driven interpretation via a physics-based, AI-accelerated approach. We validated this concept with ambient pressure X-ray Photoelectron Spectroscopy (APXPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O, as a representative example. The inputs of DTCS v.01 are designed to mirror the experimental chemists' workflows, and the outputs can be directly compared to and are constantly updated from the experimental data. This integrated theoretical and experimental platform enhances user accessibility and facilitates the acquisition of standardized mechanistic insights.

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Inelastic photoelectron scattering (IPES) by gas molecules, a critical phenomenon observed in ambient pressure X-ray photoelectron spectroscopy (APXPS), complicates spectral interpretation due to kinetic energy loss in the primary spectrum and the appearance of additional features at higher binding energies. In this study, we systematically investigate IPES in various gas environments using APXPS, providing detailed insights into interactions between photoelectrons emitted from solid surfaces and surrounding gas molecules. Core-level XPS spectra of Au, Ag, Zn, and Cu metals were recorded over a wide kinetic energy range in the presence of CO2, N2, Ar, and H2 gases, demonstrating the universal nature of IPES across different systems. Additionally, we analyzed spectra of scattering effects induced by gas-phase interactions without metal solids. In two reported CO2-reduction systems (p-GaN/Au/Cu and p-Si/TaO x /Cu), we elucidated that IPES is independent of the composition, structure, or size of the solid materials. Using metal foil platforms, we further developed an analytical model to extract electron excitation cross sections of gas molecules. These findings enhance our understanding of IPES mechanisms and enable the predictions of IPES structures in other solid-gas systems, providing a valuable reference for future APXPS studies and improving the accuracy of spectral analysis in gas-rich catalytic interfaces.

Graphene oxide (GO) is a promising membrane material for chemical separations, including water treatment. However, GO has often required postsynthesis chemical modifications, such as linkers or intercalants, to improve either the permeability, performance, or mechanical integrity of GO membranes. In this work, we explore two different feedstocks of GO to investigate chemical and physical differences, where we observe up to a 100× discrepancy in the permeability-mass loading trade-off while maintaining nanofiltration capacity. GO membranes also show structural stability and chemical resilience to harsh pH conditions and bleach treatment. We probe GO and the resulting assembled membranes through a variety of characterization approaches, including a novel scanning-transmission-electron-microscopy-based visualization approach, to connect differences in sheet stacking and oxide functional groups to significant improvements in permeability and chemical stability.

Developing functionally complex carbon materials from small aromatic molecules requires an understanding of how the chemistry and structure of its constituent molecules evolve and crosslink, to achieve a tailorable set of functional properties. Here, molecular dynamics (MD) simulations are used to isolate the effect of methyl groups on condensation reactions during the oxidative process and evaluate the impact on elastic modulus by considering three monodisperse pyrene-based systems with increasing methyl group fraction. A parameter to quantify the reaction progression is designed by computing the number of new covalent bonds formed. Utilizing the previously developed MD framework, it is found that increasing methylation leads to an almost doubling of bond formation, a larger fraction of the new bonds oriented in the direction of tensile stress, and a higher basal plane alignment of the precursor molecules along the direction of tensile stress, resulting in enhanced tensile modulus. Additionally, via experiments, it is demonstrated that precursors with a higher fraction of methyl groups result in a higher alignment of molecules. Moreover, increased methylation results in the lower spread of single molecule alignment which may lead to smaller variations in tensile modulus and more consistent properties in carbon materials derived from methyl-rich precursors.

The need to reduce atmospheric CO2 concentrations necessitates CO2 capture technologies for conversion into stable products or long-term storage. A single pot solution that simultaneously captures and converts CO2 could minimize additional costs and energy demands associated with CO2 transport, compression, and transient storage. While a variety of reduction products exist, currently, only conversion to C2+ products including ethanol and ethylene are economically advantageous. Cu-based catalysts have the best-known performance for CO2 electroreduction to C2+ products. Metal Organic Frameworks (MOFs) are touted for their carbon capture capacity. Thus, integrated Cu-based MOFs could be an ideal candidate for the one-pot capture and conversion. In this paper, we review Cu-based MOFs and MOF derivatives that have been used to synthesize C2+ products with the objective of understanding the mechanisms that enable synergistic capture and conversion. Furthermore, we discuss strategies based on the mechanistic insights that can be used to further enhance production. Finally, we discuss some of the challenges hindering widespread use of Cu-based MOFs and MOF derivatives along with possible solutions to overcome the challenges.

Activated carbon (AC)-based materials have shown promising performance in carbon capture, offering low cost and sustainable sourcing from abundant natural resources. Despite ACs growing as a new class of materials, theoretical guidelines for evaluating their viability in carbon capture is a crucial research gap. We address this gap by developing a comprehensive set of criteria that underpin the success and scalability of AC-based materials. The most critical performance parameter is the CO2 adsorption energy, where an optimal range (-0.41 eV) ensures efficiency between adsorption and desorption. Additionally, we consider thermal stability and defect sensitivity to ensure consistent performance in varying conditions. Further, selectivity and capacity play significant roles due to external variables such as partial pressure of CO2 and other ambient air gasses (N2, H2O, O2), bridging the gap between theory and reality. We provide actionable examples by narrowing down our options to methylamine and pyridine grafted graphene.

Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically heterogeneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp3 fraction, achieved via generating high-density CFs. In addition, the high sp3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.

Material properties of carbon fiber composites were linked from the nanoscale to the macroscale via molecular dynamics simulations and finite element analysis. In particular, the study focused on predicting the properties of PAN-based and pitchbased carbon fiber composite specimen. The atomistic features that develop throughout the manufacturing process were captured within the molecular dynamics simulations, while the microstructural features and composite layup information were captured within the finite element analysis model. However, in order to obtain the desired material property values, a significant amount of modeling experience is required. Therefore, the purpose of this research was to remove the finite element analysis models by training sets of neural networks to quickly and accurately predict properties from the nanoscale to the macroscale. When compared to experimental data, the errors of the high-speed composite prediction tool were all less than 10%. Ultimately, this tool can provide a rapid analysis of different fiber manufacturing techniques and composite layups to provide insight and understanding on how to better tailor the properties of carbon fiber composites for various applications.

Conductive carbonaceous membranes are a cost-effective, scalable platform to electrify membranes for fouling mitigation, contaminant degradation, and increased permeate selectivity.